Panel Data Research Center at Keio University
DISCUSSION PAPER SERIES
DP2015-003
September, 2015

Education and Marriage Decisions of Japanese Women and the Role of theEqual Employment Opportunity Act
Linda N. Edwards＊
Takuya Hasebe＊＊
Tadashi Sakai＊＊＊【Abstract】
Prompted by concordant upward trends in both the university advancement rate andthe unmarried rate for Japanese women, this paper investigates whether the Equal
Employment Opportunity Act (EEOA), which was passed in 1985, affected women’smarriage decisions either directly or via their decisions to pursue university education.
To this end, we estimate a model that treats education and marriage decisions as jointly
determined using longitudinal data for Japanese women. We find little evidence thatthe passage of EEOA increased the proportion of women who advance to university, but
strong support for the proposition that it increased the deterrent effect of university
education on marriage.

＊

City University of New York＊＊

Sophia University＊＊＊

Hosei University

Panel Data Research Center at Keio University
Keio University

Education and Marriage Decisions of Japanese Women and the Role of theEqual Employment Opportunity Act

Linda N. Edwards,
Takuya Hasebe, andTadashi Sakai*August 24, 2015

Abstract
Prompted by concordant upward trends in both the university advancement rate andthe unmarried rate for Japanese women, this paper investigates whether the Equal
Employment Opportunity Act (EEOA), which was passed in 1985, affected women’smarriage decisions either directly or via their decisions to pursue university
education. To this end, we estimate a model that treats education and marriage
decisions as jointly determined using longitudinal data for Japanese women. We findlittle evidence that the passage of EEOA increased the proportion of women who
advance to university, but strong support for the proposition that it increased thedeterrent effect of university education on marriage.
Key words: Equal Employment Opportunity Act, marriage, university education
JEL Classification: J12*

Edwards: Ph.D. Program in Economics, The Graduate Center, City University of New York. Sakai: Hosei
University, Tokyo. Hasebe: Sophia University, Tokyo. We are grateful to the Institute for Research on Household
Economics for permitting us to use the Japanese Panel Survey of Consumers for this research. We thank participantsin seminars at the CUNY Graduate Center, the Japanese Economic Association, and the Western Economic
Association International, Yukiko Abe, and especially Cordelia Reimers, for helpful comments. We also thankKaori Shiba for her excellent research assistance. This paper is a revised version of a paper presented at the June 29July 3, 2012, meetings of the Western Economic Association International.

1Introduction
The striking decline in Japanese birth rates over past thirty years has prompted national
concern, with fertility rates well below the population replacement rate (Faruqee and Mühleisen
2001; Japan Ministry of Health, Labour and Welfare 2015).1 The resulting shrinking populationmeans that in the future, the country’s old-age dependency ratio will increase as the large postwar baby boom and baby boom echo cohorts are supported by subsequent, smaller cohorts.2
Coincident with this decline in birth rates have been a decline in marriage rates (Sakamoto andKitamura 2008) and a rise in the mean age of first marriage (Japan Ministry of Health, Labourand Welfare 2015), both of which are linked directly by fertility researchers to the decline inbirth rates.3 Over the same period, with the passage of the Equal Employment Opportunity Act(EEOA) in 1985 and subsequent supporting legislation, career opportunities available to women
have expanded, especially for women with a university education.4

1

The total fertility rate reached its lowest point, at 1.26, in 2005 and though it has risen in 2014 to 1.42, it is still
well below the population replacement rate (Japan Ministry of Health, Labour and Welfare 2015).

2

The ratio of those aged 65 and above to the working-age population (aged 20-64 years) is estimated to rise from
27% in 2000 to 47% in 2025, higher than estimated for other low-birth-rate counties like France and Italy (Faruqee
and Mühleisen 2001, Table 1).

3

As many researchers have noted (e.g. Hashimoto and Kondo, 2012), because the average number of children borneby a married couple has stayed relatively constant since the 1970 and the percent of births that take place outside ofmarriage is very small (less than 2% in 2003), it is the decline in the marriage rate of women that accounts for theoverall declines in fertility. See also Narayan and Peng (2007). For a general review of models of marriage andchildbirth see Ermisch (2003) and Brien and Sheran (2003).

4

For a good review (in English) of the 1985 EEOA as well as the 1997 legislation (that went into effect in 1999)
that substantially strengthened the original law, see Araki (1998). Yamada (2013) also summarizes these two laws
and also provides a description of the subsequent 2006 law, which further expands on the original EEOA. In earlier
literature the EEOA was referred to as the Equal Employment Opportunity Law, or EEOL, as opposed to the Equal
Employment Opportunity Act, but EEOA is a more apt translation of the Japanese title for this law. With regard tolegislation covering leaves for child and elder care, Japan Ministry of Health, Labour and Welfare (2010) is a goodreference.

2

The goal of this paper is to investigate the possible role the passage of the Equal
Employment Opportunity Act in explaining the delay and decline in women’s marriage, both
directly and through the link of higher education.

Existing literature has documented theincreased proportion of women who get a university education over this period and suggested
that the EEOA may have played a role in this increase (Edwards and Pasquale 2003, Abe 2011).
At the same time, the large economic and demographic literature on the determinants ofwomen’s marriage propensity and timing underscores the role of educational attainment inmarriage decisions, with university-educated women more likely than others to delay marriage(e.g. Raymo 2003). To our knowledge, only one paper (Abe 2011) addresses the possibility thatthe passage of the EEOA could be a factor in women’s marriage decisions, but that paper does
not explicitly test this proposition. In our paper, we address this void by investigating whether
the EEOA affected women’s marriage decisions either directly or via their decisions to pursue
university education. Our model treats education and marriage decisions as jointly determined-something that has not been done in previous research on Japanese women—and is estimatedusing data from the Japanese Panel Survey on Consumers (JPSC).
Focusing on the likelihood that women marry by age 32, our research provides strong
support for the proposition that the passage of the EEOA played a role in the delay and decline ofmarriage. Specifically, even when we take explicit account of the effect of unmeasured personal
attributes on education and marriage decisions, we find that the deterrent effect of university
education on marriage more than doubles for post-EEOA cohorts of women, as compared to preEEOA cohorts. University-educated women in post-EEOA cohorts are 16 to 19 percentage
points less likely than their less educated contemporaries to be married by age 32, whereas for
pre-EEOA cohorts the corresponding decline is at most 7 to 8 percentage points. On the other3

hand, we find that the decision to obtain a university education is primarily determined by ayoung woman’s ability and a host of family background characteristic, with the EEOA having an
ambiguous and at best small impact. Overall, our findings indicate that for those seeking tounderstand the declines over the past 30 years in marriage and fertility in Japan, it is important totake into account the role played by the EEOA.
In the sections below, we review selected recent research on the relationships among
education, marriage, and the EEOA; sketch out a model of joint decision making with regard toeducation and marriage; describe the JPSC data; and provide estimates of our model using arecursive bivariate probit statistical methodology. A final section summarizes our conclusions
and suggests some implications.
Background and Related Research
The trends that prompt our research and that of many others are illustrated in Figure 1.
Shown in this figure are data from 1970 to 2013 for the total fertility rate, the percent of women
aged 30-34 not married, the percent of female high school graduates who advance to university,
and, for comparison, the percent of male high school graduates who advance to university.
Throughout this period there has been a dramatic decline in the total fertility rate which, while
increasing slightly since its nadir in 2005, still remains well below the replacement rate. At thesame time, the percent of women aged 30-34 who remain unmarried has steadily increased, fromunder 10% in 1970 to almost 35% in 2010.5 Roughly parallel with this rise in the proportion
unmarried is the increase in young women’s advancement rate to university, growing from under5

Young women’s mean age at first marriage has also been increasing over this period, from 24.2 in 1970, to 28.8 in
2010, to 29.3 in 2013 (Japan Ministry of Health, Labour and Welfare 2015).

4

10% in 1970 to over 45% in 2010. It is noticeable that the slopes of both of the latter two growth
curves become steeper after 1985, the year in which the EEOA was enacted by the Japanese Diet.
The advancement rate to university of young men also increased over the entire period, thoughless uniformly than that of women, but the difference between the advancement rates of men andwomen shrinks noticeably after 1985.
These concordant trends suggest the following set of hypotheses, which we investigate inthis paper: (1) The passage of the EEOA, by expanding career opportunities of universityeducated women, increased the proportion of qualified women who follow this educational path;(2) The expanded career opportunities associated with university education influence women’smarriage decisions, leading them to delay or decline marriage; (3) The passage of the EEOA
(and subsequent supporting legislation), which changed the legal and cultural landscape to makea career path more socially and economically attractive to women, increased the “deterrent”
effect of university education on marriage.
To explore these hypotheses, we develop and estimate a multivariate model of theinterrelationship between Japanese women’s education and marriage decisions and the role of the
EEOA in these decisions.6 No other studies directly address this set of hypotheses, but there are
a number which examine some of the relevant relationships. Below we review the studies thatmost directly inform our research.

6

Models like the one we use in this paper owe a great debt to the seminal work of Gary Becker on human capital,
marriage, and the economics of the family (see, for example, Becker, 1976 and 1993).

5

The EEOA/Labor Market Link
The hypotheses we explore are based on the assumption that the EEOA expanded career
opportunities for university-educated women. Three recent papers, Abe (2011), (2010), and(2013), investigate this assumption by looking at effects of the EEOA on women’s employment
and earnings. The findings in these three studies, described in more detail below, indicate thatthe primary strategies used by post-EEOA cohorts of women to improve their career and labormarket outcomes are to become university graduates, to work full-time (as opposed to part-time),
and to delay or decline marriage.
Abe (2011) looks at women’s labor force behavior over the life cycle using data from theJapanese Employment Status Survey (ESS, Shugyo Kozo Kihon Chosa) from 1998 to 2007. She
explores how the EEOA affected women’s full- and part-time employment patterns both bymarital status and by level of educational attainment.

Her methodology relies on comparing

cohorts of women who entered the labor market after the EEOA went into effect with earlier,
pre-EEOA, cohorts. Abe reports that the overall employment/population ratio of women has
increased for more recent cohorts of women, but that when one distinguishes between part-timework and full-time “regular” employment, most of this overall increase is accounted for by anincrease in part-time work. When the focus is narrowed to women’s employment in full-time,
regular positions-- the kinds of positions that were expected to be more available to women after
the passage of the EEOA-- and after roughly taking into account macro-economic conditions,Abe finds that only for university graduates did this employment rate increase for post-EEOA
cohorts.

6

Taking this analysis further, Abe decomposes changes in full-time employment by
marital status and finds that it was not that the full-time employment rate increased for bothmarried and unmarried university-educated women, but rather that the proportion of these highly
educated women who remained unmarried had increased.

Pulling together these threads, wehave a story in which in the post-EEOA period, women with a university education were
delaying marriage (some perhaps choosing not to marry) and, since unmarried women havehigher full-time employment rates than do married women, this delay translated to a higher fulltime employment rate of university women as a group. Abe’s findings are based on a model that
does not allow for the explicit possibility that marriage rates and educational attainment arethemselves affected by the EEOA, but she recognizes these links in her conclusion: “Since theenactment of the EEOA, more women with university education have married late or stayed
unmarried” (p. 52).
Abe (2010) targets the gender wage gap in Japan, rather than employment, and seeks todetermine the impact of the EEOA using cohort data from the Basic Survey of Wage Structure at
five-year intervals beginning with 1975 and ending with 2005. Focusing on full-time workers
only, she shows that while the overall female/male full-time wage ratio increases over this period,
this shift is mainly attributable to an increase in the educational attainment of the full-time
female labor force. For university-educated women, the female-to-male wage gap narrowed very
little for post-EEOA cohorts.
Abe (2013) is a survey paper which summarizes the result above, but also reports on
research that explores the possibility that the EEOA may have had different impacts across thevarious Japanese regions. With regard to the latter topic, she concludes that such differences doexist: the post-EEOA increase in employment rates of university-educated women that she had
7documented in earlier research was most evident in the Tokyo area, most likely because that iswhere there is the greatest availability of managerial positions.
Taken together, these studies suggest that the benefits to Japanese women of the career
opportunities enabled by the EEOA were to be obtained mainly by investing in university
education and working (especially in Tokyo) a full-time schedule, the latter of which isfacilitated by delaying or declining marriage. Abe (2013) comments that “…the EEOA did notincrease the number of women who achieve both family and career…” (p. 32), positing that the
scarcity of childcare and the costliness of commuting were likely obstacles.
Higher Education and the EEOA
The role of the EEOA law in young women’s decisions with regard to post-high-school
education is addressed in Edwards and Pasquale (2003). Using micro-data from the first wave of
the Japanese Panel Survey on Consumers (JPSC), Edwards and Pasquale’s analysis holds
constant family background, demographic factors, and economic conditions in estimating theeffect of the passage of the EEOA on the higher education decisions of young Japanese women.
Their model does a good job of explaining higher education decisions, but the results with regard
to the effect of the EEOA are not robust, in part because only two cohorts in the survey had madeeducational decisions after the passage of the law.

Nonetheless, their research provides

suggestive evidence that the passage of the law was associated with an increased propensity ofyoung women to choose university education over junior college.
Marriage
There is an extensive economic and demographic literature on women’s marriage rates inJapan, much of it focusing on explaining the secular declines illustrated in Figure 1. To our
8knowledge, none of this literature explicitly addresses the possible role of the EEOA incontributing to this decline, but a variety of other explanations have been explored. Somestudies focus on the role of labor market conditions, including unemployment rates of men,
women, or both (e.g. Higuchi 2001, Miyoshi 2014, and Hashimoto and Kondo 2012). Other
studies focus on the role of the women’s own earnings and income (e.g. Higuchi 2001, and Sakai2009). Still others focus on the increasing levels of women’s educational attainment and theresulting reduced relative availability of potential spouses with the requisite level of education—
dubbed the “marriage mismatch” hypothesis (e.g. Raymo 2003 and Raymo and Iwasawa 2005).
Other studies target increased income or other transfers (housing, for example) from parents todaughters as a potential explanation-- dubbed the “parasite single” hypothesis (Sakamoto andKitamura 2008).
These studies examine different hypotheses and use different data sets, but they have
one common feature: all find that a woman’s educational attainment is an important correlate ofwhen she marries.

Specifically, all of these studies report that women with a university

education are more likely to delay marriage.7 With regard to the question of whether this delaytranslates into a lower overall likelihood that university-educated women marry, the studies are
not definitive. Results differ depending on the set of explanatory variables held constant in theanalyses: for example, Raymo (2003) estimates a set of alternative models which yieldcontrasting results on this point.

7

Even though university-educated women delay marriage while in school, they catch up to some extent later—thedifference in mean age at marriage between university graduates and high school graduates is substantially less thanfour years (see Shirahase 2000, especially Table 1).

9Other factors that have been found to be statistically significant in one or more of thesevarious studies are: the woman’s age; measures that represent various aspects of the labor market
for both men and women, including the woman’s own income; measures that represent
socioeconomic characteristics of her parents, including their income, health, and work status;
measures that reflect income or other transfers from her parents, including housing;characteristics of the woman’s natal family; demographic measures that reflect the availability ofpotential spouses; and the region in which she lives and its rural/urban characteristics.
A Model of Joint Education and Marriage Decisions
Pulling together the findings cited above, we see that the EEOA is likely to havepositively affected the probability that women attend university; that university-educated womenare more likely than other women to be employed in full-time positions and to delay marriage;
and that a woman’s decision to marry is empirically related to her level of education, her family
background, labor market conditions at the time of her graduation and thereafter, and herearnings and income. The papers on marriage referenced above do not incorporate in theirmodels the possibility that marriage and education are jointly determined; nor do they consider
the possibility that the EEOA might be related to marriage decisions. The model described
below incorporates these innovations.
The Japanese Context
Japanese women typically choose between two types of post-high school education—
university and junior college— but it is university education that provides the background for a10career. 8 Junior college curricula are typically limited and three-quarters of them “offer a single
curriculum in non-vocational subjects, such as music, home economics, and English literature”(Ishida 1998). Junior college education is likely to be better preparation for marriage than forcareer employment, and the financial returns from a junior college education may run
predominantly through the marriage market as compared to the labor market.

University

education, in contrast, offers a curriculum that provides superior preparation for career
employment, though it too may improve a young woman’s marriage prospects.9 The education
decision we focus on, therefore, is the decision to attend or not attend university.
A distinguishing feature of Japanese higher education is that, unlike the United States
where people leave and re-enter post-high school educational institutions at various points in life,
education in Japan is more structured; few women are in any type of formal schooling after
marriage. In addition, the path to university education is well defined, so that without proper
preparation in the high school years (or even before, in some cases), a Japanese student cannot
expect to enter university.10 These features provide the setting within which education decisions
are made in Japan.

8

Other post-high school options are colleges of technology and specialized training colleges, which provide a wide
variety of vocational and practical skills but are not typically considered to be comparable to university, though insome cases they may be comparable to junior colleges.

9

In the context of the United States, Goldin (1992), Lengren and McIntyre (2006), Ge (2011) and others haveshown that a large part of the returns to university education is via the marriage market: by attending university,
young women come in contact with highly educated young men who will have greater future earning power. For
example, doing a “back-of-the-envelope” computation, Lengren and McIntyre estimate that about half of theincrease in a woman’s “available income” (including income that she receives through her marriage) associated withher own higher education comes through the marriage market.
10This characterization of access to university education is appropriate at the time the women in this sample wereattending university, but more recently there have been changes. For example, in 1997 only 5% of private
universities fell below their enrollment limits, but by 2008 the situation had changed dramatically, with 47% of

11The Model
The model we sketch out below captures in stylized form this context and is similar in
spirit to the model outlined by Lefgren and McIntyre (2006) (hereafter referred to as L&M).
L&M posit a two-period model in which a woman’s education decision is made in the first
period and her marriage decision is made in the second period.11 They also postulate that awoman’s education does not directly affect her “draw” in the marriage market, but because
higher education is associated with higher earnings, her education does affect whether or not aparticular draw from the distribution of potential husbands will be acceptable to her. The higher
her own level of education, the fewer the number of men acceptable to her as a potential spouse.
In this model, the resulting relationship between educational attainment and marriage can bepositive or negative, depending on whether a woman’s higher level of education has a stronger
effect on her own earnings or on her share of her husband’s earnings.12 L&M also show, as we
will below, that a woman’s educational choice is related to her future marriage expectations andprivate universities falling below their enrollment limits. As a result, more universities are now enrolling students
with lower test scores than would have been acceptable in the past; such universities have been dubbed “free-pass”
universities by the Japanese media. For a detailed discussion of recent changes in Japanese higher education see
Igami (2014).
11

L&M apply this two-period model to data for the United States, but the model is more appropriate in the context
of Japan than it is for the United States, where it is not at all uncommon for people to enter and/or reenter university
after marriage or after having had children.

12

L&M use an instrumental variable strategy to estimate their model using data from the 2000 U.S. Census ofPopulation. They find that women’s education “appears to have a substantial causal effect on husband’s earnings”(p. 788) but has a relatively small impact on a woman’s probability of marriage. Another paper that looks at theinterrelationship between education and marriage decisions in the U. S., Ge (2011), focuses on the increased
financial gains from marriage obtainable by attending college (because of the better set of potential spouses fromwhich to choose). Ge reports that the expected financial gains from marriage are a significant determinant of awoman’s decision to attend college, but has a much less impact on her college completion.

12

that not taking into account this potential endogeneity can lead to biased coefficients of theeducation variable in a marriage equation.
While our model is inspired by L&M, it differs because we focus on tracing the effects ofthe EEOA on the interrelated decisions regarding education and marriage rather than on
measuring the economic status of women before and after marriage. We assume that a young
woman’s (and her family’s) decision with regard to whether or not she will get a university
education is well defined by the time she is near the end of high school-- at age 17 (this age
corresponds to period one in L&M’s model). Variables that affect this decision would include
family demographic and socioeconomic characteristic, the expected costs and returns to auniversity education, and unmeasured ability and taste factors that reflect a young woman’s
desire for career employment and marriage. The marriage decision is assume to take place after
her education is completed (this corresponds to period 2 in the L&M model), and is determinedby the young woman’s educational attainment (which, in line with L&M, will affect her financial
returns to marriage), her family background, various indicators of the states of the marriage andlabor markets, and unmeasured taste and culture factors that influence both her career aspirations
and her judgment about the desirability of marriage.
The features described above are best captured by a recursive bivariate probit statisticalmodel, represented mathematically below (see Greene 2008, pp. 823-826 for a discussion of thismodel).
∗

,

1 if

∗

0, 0 otherwise,

∗

,
~

0
,
0

1 if

∗

0, 0 otherwise,1
1
13

In this system of equations, the dichotomous variable
woman completed university,13

represents whether or not a young

represents whether or not she has been married by the age of3214, and y* and z* denote latent variables of y and z, respectively. The factors which affect theeducation decision, denoted by
the error term

, are similar to those in Edwards and Pasquale (2003), while

picks up unmeasured ability, tastes for education, taste for marriage, and taste

for career employment, all as of the time the young woman is making her higher education
decision. The factors that affect the marriage decision, denoted by x m , follow closely themarriage literature cited earlier, while the random error term,, picks up various luck factorsthat determine a marriage match and also the young woman’s unobservable taste for career
employment and marriage at the time of completing her education. The variables inandwhich have some common elements, are described in detail in the next section.

,Thedichotomous variable EEOA, appearing in both equations, indicates whether or not a young
woman’s education decision was made before or after the passage of the EEO Act.
There are two other things to note about the econometric model. First, the error terms in
the education and marriage equations may be correlated because they both include componentsthat represent unmeasured tastes for marriage and career employment. Such a correlation implies
that educational attainment is an endogenous variable in the marriage equation (Cov[y,]  0).

13

Like L&M, we posit these relations in the form of regression equations. In an appendix, L&M sketch out how
regression equations such as these could be derived, with a set of appropriate simplifying assumptions, from a utility
maximization framework. Note also that the first equation in the system is similar to the estimating equation in
Edwards and Pasquale (2003), which is derived from a random utility model.

14

In this paper, since we are focusing on the marriage decision, we define our marriage variable to include anyone
who at the point when we observe her had decided to become married, whether or not that marriage ended in divorce.
Note that divorce is relatively rare in Japan, at about 2 per 1000 population in 2010 (Japan Ministry of Health,Labour and Welfare 2015). In the JPSC data, approximately 1.0% of the previously married women get divorced
every year.

14Indeed, including the education variable, which is the dependent variable in the first equation, inthe marriage equation as an explanatory variable is what distinguishes this statistical model froma non-recursive model.15 Second, we include an interaction term between the education variable
and the EEOA variable, as explained below, in order to see if the effect of education on marriagechanges after the passage of the EEO Act.
Data and Variables
The data used to estimate our model come from a unique micro-level panel survey
entitled the Japanese Panel Survey of Consumers (JPSC), a nationwide longitudinal survey ofyoung Japanese women and their husbands sponsored by the Institute for Research on Household
Economics (Kakei Keizai Kenkyujo) in Japan. These data are especially suitable for our study
because they provide a rich set of information about women’s family background, education, andmarriage. The first wave (Wave A) of this survey was conducted in 1993 and included 1500
randomly selected women aged 24-34 in that year.16 Subsequent waves (B and C) were added tothe sample in 1997 and 2003: Wave B included 500 women aged between 24 and 27 years in
1997; and Wave C included 836 women aged between 24 and 29 years in 2003. As of 2008,
there remained approximately 1,650 respondents aged between 29 and 49 in the JPSC.
The structure of the data set is illustrated in Figure 2. Each row in the figure corresponds
to a year and shows the number of women of each age for whom data are reported for that year.

15

Green (2008) notes, however, that in models such as this one, the endogenous nature of education variable inmarriage equation “can be ignored in formulating the log-likelihood” (page 823). Hence, in the estimation we areable treat the education variable in marriage equation as if it were exogenous.
16

The survey originally contacted 3,623 randomly selected women in this age group, of whom 1500 were ultimately
selected to be in the first wave of the panel. Demographic characteristics of these participants were comparable tothose of the same age group in the Population Census (Higuchi 2001).

15

For example, in 1993, the first survey year of Wave A, there were 151 women aged 24, 161 aged
25, and so on, for a total of 1500 women aged 24 to 34 in that year. In the following year, 1994,
the women have aged one year and there is some attrition, so that there are no women aged 24,
145 aged 25 (6 women from that age-cohort had dropped out of the survey over the year), 146
aged 26, and so on, for a total of 1415 women aged 25 to 35 in that year (total attrition was 85).
Things continue in a similar fashion in 1995 and 1996. Wave B begins in 1997, with a newgroup of 24 to 27 year-old women added to the survey, and Wave C begins in 2003, with anadditional group of women aged 24 to 29 added in that year. From this diagram we can computethat the potential number of women for whom we would have family background and education
information-- both of which come from the questionnaire administered in the initial survey year
for each wave-- is 2836. Because of missing observations for some of these variables, our actual
sample consists of 2598 women.
Looking at Figure 2 in a slightly different way, one can see that each column shows thenumber of observations available for women of a specified age but at different points in calendar
time. For example, if one wanted to study women at age 32, there would be 122 of themobserved in 1993, 124 observed in 1994, and so on, for a total of 1641 women in the sample who
responded to the survey at age 32. Also indicated in this figure is whether women of a particular
age in a particular year are members of the pre-EEOA cohort or the post-EEOA cohort. The preEEOA cohort is defined to be women aged 18 or older in 1985, the year that the EEO Act was
passed; women in this cohort appear in the figure above the dotted diagonal. The post-EEOA
cohort of women is defined to be those who were aged 17 or younger in 1985; women in thiscohort appear below the dotted diagonal.

16

Our choice of marriage variable—whether a woman is or has been married by age 32—
requires some explanation. Ideally, we would observe marital status at an older age because not
all women who plan to marry will in fact be married by age 32. 17

However, given theconstruction of the sample and sample attrition, the later the age at which we observe marital
status, the fewer observations will be available. Further, if we choose to observe marital status at
a later age, the balance between the pre-EEOA and post-EEOA samples is reduced. Thus, ourchoice is a pragmatic one: by observing women at age 32, we will capture a large proportion of
marriages while still having a large enough sample size to address our main hypotheses.18
Among our working sample of 2598, there are 2157 women for whom marital status atage 32 can be determined. The difference between these two numbers is attributable primarilyto: (1) women who remained in the survey through 2008 but had not yet reached age 32 and had
not yet married; and (2) women who had dropped out of the sample before age 32 and had notmarried prior to dropping out.
The variables used in our estimation are defined in Table 1, along with the data sources
for variables that are not taken from the JPSC. The variables that do not come from the JPSC are
measured at the level of the prefecture in which the young woman resided as of age 17. The final
two columns of the table indicate whether the variable appears in the education equation, themarriage equation, or both.

17

Because of the lock-step nature of higher education in Japan, we can be confident that women who had chosen
university education will have completed it by age 32. Indeed, only 28 people in the entire JPSC sample areenrolled in any kind of schooling after the age of 32.

18

The mean age at first marriage for women in Japan over the time period covered in our data ranged from 25.9 (in1990) to 28.8 (in 2010) (Japan Ministry of Health, Labour and Welfare 2015).

17

The variables inare similar to those in Edwards and Pasquale (2003) and include:

characteristics of the woman’s family backgroundincome,(parents’ educational attainment, family

whether the young woman attended private high school, her number of siblings,

whether she has any brothers, and whether her mother was primarily a homemaker); proxymeasures of her academic ability (attendance at juku (“cram school”) in elementary (Juku 2),
junior high (Juku 3), and high school (Juku 4)); proxy measures for the availability andopportunity costs of university education in her area as measured at her age 17 (the ratio ofprofessors to high school graduates and the vacancy/application ratio); a proxy for the expected
returns to university education (the ratio for males of the starting wage for university graduates
relative to that of high school graduates)19; and a dummy variable indicating whether the EEOA
was in effect when she was 17 years old, which is the age at which we assume her final decision
with regard to university education is made. As discussed earlier, the latter variable is included
because the EEOA aimed to increase women’s access to career employment (and the resulting
higher lifetime earnings), and university education is the traditional route to this type of
employment.
In the case of the marriage equation, the explanatory variables xm represent factors
suggested by the economic and demographic literature surveyed in the previous section.
Educational attainment has been found to be an important variable in marriage decisions inalmost all of the literature that we surveyed and falls directly out of the L&M utility
maximization model described above; our education dummy variable indicates whether or not
the young woman completed university. Family background variables like parents’ income,19

This variable is not specific to the prefecture, but is rather a national average.

18

family structure (number of siblings and whether there is a male sibling), and whether the
woman’s mother was a full-time homemaker are also commonly used. To represent the state of
the labor market around the time that the young woman completes her education a variety ofproxies have been used (see Higuchi (2001), Hashimoto and Kondo (2010), and Sakamoto andKitamura (2008)). We use the prefecture vacancy/application ratio at the age she completes her
schooling to proxy the strength of the labor market she faces post-schooling. 20 A higher
vacancy/application ratio indicates a stronger job market, which may be positively or negatively
related to the probability of marriage.21 In addition, following Abe (2013), we include two city
size variables to proxy the state of the labor market for university-educated women.
To capture the state of the marriage market we use several variables. The availability ofpotential spouses with a level of education equal to that of the woman’s (found to be an
important factor by Raymo and Iwasawa 2005) is computed for each birth cohort for eachprefecture as follows: for women who did not have a university degree, we use the ratio of thenumber of (two years older) male high school graduates (with or without a university education)
to female high school graduates (without a university education);

for women who had auniversity degree, we use the ratio of the number of (two years older) male university graduates
to female university graduates.22 We expect this variable to be positively related to the woman’s
probability of marriage. The cost of setting up a household is proxied by rent per tatami mat (in20

The age at which a woman’s education is completed is assumed to be 18 for a high school graduate, 21 for ajunior college or vocational school graduate, and 23 for a university graduate.

21

Miyoshi (2014) and others, noting that a strong labor market affects both a woman’s expected earning power andthe earning power of a potential spouse, refers to the positive relationship the “self-reliance effect” and the negative
relationship the “good catch effect”.

22

It is difficult to find data that would have allowed us to compute a more refined measure of spouse availability at
the prefecture level within Japan.

19

constant yen) in the woman’s prefecture as of the year she completes her education. The searchcosts associated with finding a mate and also varying cultural norms regarding marriage areproxied by the two city size variables mentioned above (Sakai 2009).
To explore the potential impact of the EEOA, we include in the marriage equation the
EEOA dummy variable as defined above as well as an EEOA/education interaction term. This
interaction term, which enables us to estimate separate education coefficients for pre- and postEEOA cohorts, permits us to test the hypothesis that the passage of the EEOA increased the“deterrent” effect of university education on marriage.
In addition to these variables we include in both the education and marriage equations a
set of dummy variables that indicate the geographic district in which the woman lived when she
was aged 17 (Japan is divided into ten such districts).23 These are included to hold constant any
district-specific unmeasured taste, economic, or cultural factors that may affect education or
marriage decisions. Finally, in some specifications, we include a linear time trend variable.
Descriptive statistics for all variables are shown in Table 2.
Results
Table 3 presents maximum likelihood estimates of our model.

In discussing theseestimates, we concentrate on the sign and significance of the coefficients and on comparing thetwo specifications. We do not discuss the magnitude of the probit coefficients because they are23

It is possible that women will not be living in the same district at the time they make their marriage decision as
when they were age 17, but the JPSC data do not permit us to identify the district in which each woman lives
subsequent to age 17. The ten districts (called Chiho in Japanese) are Hokkaido, Tohoku, Minami-Kanto, KitaKanto&Koshin, Hokuriku, Tokai, Kinki, Chugoku, Shikoku and Kyushu.

20

not readily interpretable. To evaluate magnitudes requires the estimation of partial effects,
which appear in Table 4.
We begin with the estimates in column 1. 24 Looking first at the education equation, we
see that, overall, the variables do a good job of explaining young women’s decisions with regard
to university education: most of the coefficients are statistically significant and have signs
consistent with our expectations and with the findings of Edwards and Pasquale (2003). 25
Family background variables such as family income, parents’ education, and number of siblings
are significant predictors of the probability that a young woman will attend university, with apositive association between parents’ income and education and a negative association between
family size and education.

Having a brother has a negative sign, but is not statisticallysignificant. However, having a mother who was a full time homemaker is positively andsignificantly related to a young woman’s likelihood of attending university; perhaps having one’s
mother available full time facilitates the young woman’s study. Surprisingly, attending a private
high school, which is included as a supplementary wealth measure and also may represent theparents’ taste for education, has a negative and significant relationship with the young woman’s
probability of attending university. With regard to academic ability, attending juku in elementary
and/or junior high school are indicators of a lower than average level of ability, and attending
juku in high school is an indicator that the young woman has a high enough level of ability to
24

The education equation is estimated using our entire sample of 2598 observations, while the marriage equation
uses the 2157 observations for which marriage data are reported. That is, the observations without marriage
information contribute to the likelihood function of education only. The main difference between these two samples
is that the smaller sample is slightly older and (consequently) has a slightly lower proportion of university graduates.
Put differently, those who do not report marital status are more likely to be from recent cohorts and more likely to beuniversity graduates.

25

Note that Edwards and Pasquale (2003) is not perfectly comparable with this paper because it employs threeeducation categories (university, junior college, and all other education) and a logit econometric model.

21

contemplate university. 26

The coefficients of all three juku variables have signs in the expected

direction--negative in the case of Juku 2 and Juku 3 and positive in the case of Juku 4, --and thelatter two are statistically significant.
The variables included to capture the returns to and costs and availability of university
education do not have statistically significant coefficients.

The vacancy/application ratio

observed when the woman is aged 17, used to proxy the opportunity cost of attending university,
has a negative sign as expected but not statistically significant; the negative sign means that thehigher this ratio-- corresponding to a more favorable labor market-- the less likely is a young
woman to attend university. The variable included to proxy the “supply” of university places inthe young woman’s prefecture, the professor/high school graduate ratio, which we expected tohave a positive sign, has a negative sign but it is not statistically significant.

The variable

included to proxy the expected returns to a university education-- the ratio of the starting wage
for male university graduates to the starting wage for male high school graduates-- has a positivecoefficient, as expected, but is not statistically significant. It may be that these variables do notwell represent the underlying costs and returns factors we are trying to measure.
Finally, the EEOA variable has a positive and statistically significant coefficient,
implying that young women who were making their higher education decisions after the EEOA
had been passed were more likely than comparable women from earlier cohorts to attend
university.
Estimates of the marriage equation appear below those of the education equation.
Consider first the role of having a university education.
26

Consistent with the studies of marriage

See Edwards and Pasquale (2003) for a more detailed discussion of using attendance at juku as a proxy for ability.

22

cited earlier, we find that women’s university education has a negative association with marital
status by age 32. However for pre-EEOA cohorts this relationship is not statistically significant.
Conversely, the coefficient of the Education/EEOA interaction term is negative and significant,yielding the result that for post-EEOA cohorts there is a statistically significant negative effect of
university education on marriage. To be more specific, the probit coefficient for pre-EEOA
cohorts is -.324 and not statistically significant, while for post-EEOA cohorts it is more than
twice as large, at -.680 (-.324 + (-.356 )), and statistically significant. This result is consistent
with the hypothesis that the “deterrent effect” of university education on marriage increases
significantly after the passage of the EEOA. Interestingly, the EEOA dummy variable itself is
not statistically significant, indicating that the EEO Act had no added effect on marriage beyond
that which operates through university education.
The role of the family background variables is mixed.

Higher family income isassociated with a lower probability of marriage, as is having a mother who is a full-time
homemaker, but these are not statistically significant relationships. In contrast, having moresiblings has a significant positive relationship to the probability of marriage, while having atleast one brother has a significant negative relationship with the probability of marriage. Having
more siblings may reduce a young woman’s responsibilities with regard to caring for aged
parents, thereby making it more feasible for her to marry. It is unclear why having a brotherwould be negatively associated with the likelihood of marriage.
Other variables in the marriage equation are proxies for aspects of the marriage market
and/or the labor market. The two city size variables—large city and medium city-- reflect
unmeasured aspects of both the labor market and the marriage market. The signs of bothvariables are negative and significant, with the coefficient of large city greater in absolute value
23

than the coefficient of medium city. This result implies that the larger the city in which a woman
lives the less likely she is to have married by age 32, a result consistent with the findings in Sakai(2009). Rental costs are also significantly related to the likelihood of marriage and in the
expected direction, with higher rental costs associated with a lower probability of marriage. The
other measure that proxies the state of the marriage market, spouse availability, is not statisticallysignificant and its sign is the opposite what we expected: the greater the availability of potential
spouses, the lower the probability of women’s marriage. The vacancy/application ratio, included
to capture the state of the labor market, has a negative, statistically significant sign, consistent
with the findings of Higuchi (2001) that women are less likely to marry when the job market isstrong.
Two other findings in Table 3 should be pointed out. First, the estimated value for thecoefficient of correlation between the error terms in the education and marriage equations is
positive but small (.033) and not statistically significant.

This means that the potential

correlation between unmeasured characteristics of the young woman that affect both educationand marriage is not large enough to affect our estimates. Second, more than half of the district
dummy variables are statistically significant, indicating that it is important to include thesevariables to hold constant cross-sectional social and economic differences that are not fully
captured by the socioeconomic variables included in the analysis.
We next explore a variation in our model. One could argue that the results in column 1
with regard to the relationships among the marriage, education, and EEOA variables are simply
reflecting secular trends in cultural attitudes towards the role of women in society rather than any“cause and effect” relationship among these three variables. Put differently, it could be argued
that the EEOA variable in both equations and the education and education/EEOA variables in the24marriage equations are simply proxies for this secular change in attitudes rather being in ofthemselves factors in women’s decision making. If this argument were true, a trend variable
added to our estimating equations would be statistically significant and knock out some or all of
the other variables that have monotonic trends.
We carry out this robustness test by adding a trend variable to the specifications incolumn 1 of Table 3. This variable (“Trend”) is coded at one for the oldest cohort in our data
(those born in 1959) up to 21 for the youngest cohort (born in 1979). The resulting estimatesappear in column 2. As might be expected, the coefficient of Trend has a positive sign in the
education equation and a negative sign in the marriage equation. However, in both cases thecoefficients are not statistically significantly different from zero. Thus, adding this variable does
not contribute significant explanatory power to our economic model.

What including thisvariable does do, however, is to sap some strength from the EEOA variable.

Specifically, thecoefficient of the EEOA variable in the education equation drops to virtually zero and loses
statistical significance. Notably, what is not affected by the addition of Trend is the coefficientof the education/EEOA interaction in the marriage equation; this interaction maintains its
statistically significant negative sign (though its coefficient shrinks slightly in absolute value).
Further Results: Partial Effects
We have now identified the statistically significant variables that influence the university
education and marriage decisions of young women, but it is well known that statisticalsignificance does not necessarily translate into practical economic or social impact. To see
which variables are of consequence to decision making, we compute partial effects. These
partial effects are shown in Table 4 and are computed as follows.

In the case of binary25

explanatory variables, the partial effect is the change in the value of the dependent variableassociated with shifting the value of the binary variable from zero to one while holding constant
all other explanatory variables. In the case of non-binary discrete variables, the partial effect isthe change in the value of the dependent variable associated with a one unit increase in the
explanatory variable, again holding constant all other explanatory variables. For both types of
discrete explanatory variables, we compute the partial effects using the finite-difference method.
For continuous explanatory variables, we compute the partial effects using calculus method. In
all cases, we compute the partial effects for each observation and then compute of the averagesof these effects to yield average partial effects (see Greene (2008) or Hasebe (2013) for details ofpartial effects in the recursive bivariate probit model). Standard errors are computed using thebootstrap method with 100 replicates.
There is an added wrinkle to computing partial effects in a recursive model: variables that
affect the education decision also affect the marriage decision, but indirectly, via education’s
impact on marriage.

Therefore, for the marriage equation three types of partial effects areshown: direct partial effects (col. 1)—those that pertain to the explanatory variables () thatappear in the marriage equation--; indirect partial effects (col. 2)—those that pertain to
explanatory variables ( ) in the education equation--; and total partial effects (col. 3)—the sumof direct and indirect partial effects. In the case of the education equation, there are no indirect
partial effects, so the direct and total partial effects are the same.
The partial effects reported in Table 4 correspond to the two specifications in Table 3:Panel A contains partial effects computed from the coefficient estimates in column 1 (trend
variable excluded), while Panel B contains those computed from those in column 2 (trend
variable included).

In our discussion of this table we focus on the partial effects that are26

statistically significant, but for the interest of the reader, we have included partial effects for all
variables. Recall that partial effects are computed for each variable holding constant all othervariables and are therefore not additive.
To get a sense of the scale of these partial effects, it is useful to keep in mind the level
and changes in the prevalence of university education and marriage across the cohorts in our
sample. The percent of women in our sample with a university education ranged from 11% in
the earliest cohort to 25% in the final cohort, an increase of 14 percentage points. Over the same
period, the percent of 32 year-olds ever married ranged from 100% in our earliest cohort to 73 %in the final cohort, a decline of 27 percentage points.
The first thing to point out about Table 4 is that for many of the variables, the partial
effects are virtually the same in both Panels A and B. Much of our discussion, therefore, does
not distinguish between the results in the two panels. Only when there are differences do we
discuss the results in Panels A and B separately.
Consider first the education equation. It is evident in Table 4 (both panels) that family
background variables play the strongest role in decisions regarding university education. The
partial effect of mother’s education is by far the largest: having a mother with a university
education is associated with an increased probability that a young woman herself completes
university by 24 percentage points. Having a father with a university education is almost as
powerful, associated with a 18 percentage point increased likelihood of completing university, as
is attending juku in high school, which is associated with a 19 percentage point increase in thelikelihood of completing university. Being in a high income category is also associated with alarge partial effect, as compared to being in the lowest income category, at 10 percentage points.

27

Other family background variables that are statistically significant have lesser partial effects,
ranging from 2 to 4 percentage points.
There are two variables in the education equation for which the results in Panels A and B
differ. The first is the vacancy/application rate, which has a statistically significant partial effectof -.031 in Panel A and a non-significant partial effect of -.028 in Panel B. This difference,
however,ifoflittlepractical

import:aone-standard-deviation

increaseinthevacancy/application rate is associated with a decline of 1.4 percentage points in the first case and1.2 percentage points in the second case.
The other significant variable for which there is a difference between the two panels is
EEOA. In Panel A, being a member of the post-EEOA cohort is associated with a 3 percentage
point higher likelihood that a young woman will complete university, as compared to membersof the pre-EEOA cohort, whereas in Panel B this partial effect loses significance and is reduced
to virtually zero. Thus, with this range of partial effect estimates for the EEOA variable—0 to 3
percentage points—we cannot draw an unambiguous conclusion about the importance of the
EEO Act on young women’s decisions regarding university education.
Moving to the marriage equation, consider first the direct partial effects. The single mostimportant variable, in terms of the magnitude of the partial effect, is education. For pre-EEOA
cohorts a university education is associated with a 7 to 8 percentage point reduction in thelikelihood that a young woman has married by age 32, though this partial effect is notstatistically different from zero. For post-EEOA cohorts, however, the partial effect is muchlarger and statistically significant—ranging from -16 to -19 percentage points. Given the factthat in our sample the proportion of 32-year-olds who have married falls by 27 percentage points

28

over the period in our study, the magnitude of this latter partial effect is remarkable. This resultclearly supports the proposition that university-educated women believe that they can best takeadvantage of the enhanced career options associated with the passage of the EEO Act bydelaying or declining marriage.
Among the other statistically significant direct partial effects, city size is important:
women from large cities are 5 percentage points less likely to be married as compared to small
cities, and the corresponding difference is 4 percentage points for middle-sized cities versus
small cities. The number and gender of siblings also has a similar impact: having an additional
sibling is associated with about a 4 percentage point increase in the probability of marriage,
while having at least one brother is associated with a 3 percentage point decline. The partial
effects of the rent and labor market variables are comparable in magnitude to those of these two
family structure variables: a one standard deviation increase in monthly rent (which corresponds
approximately to a one thousand yen increase) is associated with about a 4 percentage point
decline in the likelihood of marriage, and a one standard deviation increase in thevacancy/application ratio is associated with a decline of 3 percentage points. In contrast to thecase of the education decision, for the marriage decision the partial effect of the EEOA dummy
in both Panels A and B is never significantly different from zero.
Beyond these direct effects, a number of variables have significant indirect effects on
marriage through their effects on education, with their sign in marriage equation opposite to theirsign in the education equation. These tend to be of a smaller magnitude than the direct partial
effects and with one exception, are statistically significant only in Panel A,

The exception is

the mother’s education variable: women whose mothers were themselves university graduates
have a 3.2 to 3.7 percentage point lower probability of marriage by 32, as compared to other29

women.

In the case of the six variables where both direct and indirect partial effects on

marriage are pertinent—middle income, high income, homemaker, sibling, brother, and EEOA--,
the contribution of the direct partial effect dominates the contribution of the indirect partial effectso that the conclusions we drew when we discussed the direct effects remains unchanged.
What conclusions can we draw from these results? First, young women’s decisions withregard to university education are determined primarily by her parents’ education and income, by
the young woman’s ability, and by her family’s structure. Economic factors that reflect the costs
and returns to education do not play a significant role in this analysis. The role of the EEOA is
unclear: with a range of estimates of partial effects of 0 to -3 percentage points, our results aresuggestive but inconclusive.
Second, it is clear that young women’s decisions with regard to university education andmarriage are closely interlinked. The single most important variable from among those we study
in determining whether a woman is married by age 32 is whether or not she has a university
education, but notably, this strong linkage is found only for post-EEOA cohorts. For pre-EEOA
cohorts, university-educated women are estimated to be 7 to 8 percentage points less likely tomarried by age 32, compared to their less-educated contemporaries, but this estimate has such alarge variance that it is not statistically significant. In contrast, for post-EEOA cohorts, we see astrikingly large, statistically significant partial effect of university education on marriage by age
32, in the range of 16 to 19 percentage points. There is also an intergenerational aspect to the roleof education in marriage which operates indirectly, with women whose mothers or fathers had auniversity education approximately 3 percentage points less likely to be married by age 32.

30

Third, other factors affect marriage decisions by age 32, but to a lesser extent. Marriage isless likely for women who live in large or middle-sized cities or who have a male sibling. In
contrast, having additional siblings (holding their sex constant) is associated with a higher
likelihood of marriage.

The role of the labor market is similar to that reported by otherresearchers: when the vacancy/application rate is higher, and jobs more plentiful, women are lesslikely to be married by age 32. Also, when the cost of setting up a marital home, as reflected byaverage rental costs, is higher, women are less likely to be married by that age. The passage of
the EEOA does not appear to have had an important impact in of itself, but rather operates by
increasing the responsiveness of the marriage decision to university education, as describedabove.
Conclusions
Prompted by declines in Japanese birth rates and marriage rates over the past thirty years,
this paper seeks to understand how women’s declining propensity to marry interacts with thegrowth over the same period in women’s propensity to attend university, and how both of theselatter two trends may have been impacted by the passage in 1985 of the Japanese Equal
Employment Opportunity Act (EEOA). Using data from a unique Japanese panel survey, theJapanese Panel Survey on Consumers (JPSC), we estimate a model that treats education andmarriage decisions as jointly, though not simultaneously, determined. Specifically, we use arecursive bivariate probit econometric model to capture the particular context within which
education and marriage decisions are made in Japan.
Our empirical results provide support for the second and third hypotheses we set out
earlier, but not for the first. In the case of our first hypothesis, our evidence about the role of the31

EEOA in university-education decisions does not provide unambiguous support. In the case of
the second and third hypotheses, we find that women who were university educated had a lower
probability of being married by the age of thirty-two, as compared to other similar women, andthat the deterrent effect of university education is significantly greater for post-EEOA cohorts
than for their predecessors. Overall, our research strongly suggests that the Japanese Equal
Employment Opportunity Act and the expansion in career opportunities it made available touniversity-educated women was a contributory factor in the decline over the past 30 years ofmarriage in Japan.

32

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36

Figure 1. Four-year University Advancement Rate, Unmarried Rate and Fertility Rate
%
60.0

2.5

50.0
2

40.0
1.5

30.0

1
20.0

0.5
10.0

0

0.0
1970

1975

1980

1985

1990

1995

2000

2005

2010

Percentage of male students advancing to four-year universities

Percentage of female students advancing to four-year universities

Female unmarried rate (age 30-34)

Total fertility rate (right axis)

Source: Basic School Survey (Ministry of Education, Culture, Sports, Science and Technology),
Vital Statistics (Ministry of Health, Labour and Welfare),Population Census (Ministry of Internal Affairs and Communications)37

Figure 2. The Number of the JPSC Respondents by Age in each Year
Year 24 25
1993 151 161
1994
145
1995
1996
1997 125 131
1998
110
1999
2000
2001
2002
2003 127 135
2004
106
2005
2006
2007
2008

26
27
28
29
145 115 144 125
146 136 108 136
134 139 127 100
132 128 122
127 117 126 121
121 107 97 118
104 110 104 88
99 103 99
94
97
88
152 140 139 143
113 134 119 124
94 107 126 111
87
99 116
82
94
76

30
143
115
133
98
121
121
107
82
93
88
81
128
119
98
107
90

31
132
138
109
126
96
114
114
108
80
91
82
77
117
113
94
100

32
122
124
130
107
121
91
107
109
105
76
88
79
75
108
109
90

33
106
117
119
128
103
112
88
105
106
100
72
85
72
73
102
105

34
156
101
109
107
128
95
109
85
102
102
94
71
78
65
70
99

35

Age
36
37

38

39

40

41

42

43

44

45

46

149
96 145105 93 143
103 103 92 135
122 9897 91 134
88 117 95 93
88 125
106 88 112 91
91
84 11982
97
84 108 89
86
83 115
101 82
92 83 105 87
84 82 112
97
97
81 87
77
99
85 81
76 106
90
94
95 76
85
70
95 82
79
76 102
67
84
90 93
71
84
68 92
78
75 70 99
75
65
82 85
88
69
82 68
91
75 72 69
63
73
65 82
85
87
70 80
64
84 72 67
66
63
70 61
82
84
83 67
81
64 80 72

47

94
66
64

Total

48

49

90
63

1,500
1,415
1,341
1,289
1,749
1,628
1,537
1,481
1,421
1,373
2,139
1,980
1,870
1,774
1,706
88 1,648

WAVE A:WAVE B:WAVE C:

Post-EEOL cohorts are in dotted box.

38

Table 1. List of Variables
Variable Name

SourceCompleted education

JPSC

Marital status

JPSC

Parents' income

JPSC

Mother's education
Father's education
Private high school

JPSC
JPSC
JPSC

Homemaker

JPSC

Number of siblings
Having brother(s)

JPSC
JPSC

Juku 2, Juku 3, Juku 4

JPSC

Districts 1-10

JPSC

Time Point of
Measurement

Variable Description

University and above, 1; junior college andbelow, 0.
At the age of thirty-two Ever married, 1; never married, 0.
Parents' income in the previous year--3 dummy
variables with "low income" as the reference
category.
High income: above 10 million yen per year, 1;
The initial survey year* otherwise, 0.
Middle income: between 2.5 million and 10
million yen per year, 1; otherwise, 0.
Low income: below 2.5 million yen, 1;
otherwise, 0.
The initial survey year* University, 1; otherwise, 0.
The initial survey year* University, 1; otherwise, 0.
The initial survey year* Attended private high school, 1; otherwise, 0.
During daughter's childhood (birth to age 20),mother was never employed for pay, 1; during
The initial survey year*
daughter's childhood, mother was at some point
employed for pay, 0.
The initial survey year* Number of siblings
The initial survey year* Has one or more brothers, 1; otherwise, 0.
Juku 2: attended juku in the late years ofelementary school, 1; otherwise, 0.
Juku 3: attended juku when in junior high school,
The initial survey year*
1; otherwise, 0.
Juku 4: attended juku when in high school, 1;
otherwise, 0.
District the respondent was living in when achild--9 dummy variables with District 1 as thereference district:
The initial survey year* 1) Hokkaido, 2) Tohoku, 3) Southern-Kanto, 4)
Northern-Kanto & Koshin, 5) Hokuriku, 6)Tokai, 7) Kinki, 8) Chugoku, 9) Shikoku, 10)
Kyushu.
The initial survey year*

Education Marriage
Eq.
Eq.
Yes

Yes

No

Yes

Yes

Yes

Yes
Yes
Yes

No
No
No

Yes

Yes

Yes
Yes

Yes
Yes

Yes

No

Yes

Yes

39

Large city

JPSC

The initial survey
year*

Medium city

JPSC

The initial survey
year*

EEOA

JPSC

Trend (Cohort trend)

JPSC

Number of professors**

Basic School Survey(Ministry of Education,
Culture, Sports, Science andTechnology)

Basic School Survey(Ministry of Education,
Culture, Sports, Science andTechnology)

Spouse availability**

Vacancy/application

ED**

Vacancy/applicationMA**

Job/employment placement
services statistics (Ministryof Health, Labour andWelfare)Job/employment placement
services statistics (Ministryof Health, Labour andWelfare)-Living in Tokyo's 23 wards or in one of theother 13 major Japanese cities, 1; otherwise,
0. (Te reference category for the two citysize variables is "towns, villages or overseas".)Living in cities other than "large" cities, 1;
otherwise, 0.
Those who were 17 or younger in 1985 (theyear the EEO Law was passed), 1; otherwise;0
Those who were born in 1959 (the oldest
respondents) were coded at 1, and so on up to
those who were born in 1979 (the youngest
respondents), who were coded at 21.

No

Yes

No

Yes

Yes

Yes

Yes

Yes

At the age ofseventeen

Number of professors per high school graduate

Yes

No

At the age ofseventeen

For respondents who did not have a university
degree: the ratio of (two-years senior) malehigh school graduates to female high school
graduates who did not go to university. For the
respondents who have a university degree: theratio of (two-years senior) male high school
graduates who went to university to female
high school graduates who went to university.

No

Yes

At the age ofseventeen

Ratio of job offers to job seekers

Yes

No

At the age ofcompleting education#

Ratio of job offers to job seekers

No

Yes

University/high-school first wage ratio

Basic Survey on Wage
Structure (Ministry of
Health, Labour and Welfare)

At the age ofseventeen

Ratio of university graduate's first wage to highschool graduate's first wage for males (national
average)

Yes

No

Rent**

Housing and Land Survey ofJapan (Statistics Bureau,
Ministry of Internal Affairs
and Communications)

At the age ofcompleting education#

Real rent per tatami mat, in thousands of yen (atatami mat is approximately 1.7 square meters).

No

Yes

* The JPSC has four different survey waves. The initial survey year is 1993 for Wave A, 1997 for Wave B, 2003 for Wave C, and 2008 for Wave D.
** indicates that a not-JPSC variable is aggregated at prefecture level.
# The age at which education is completed is assumed to be 18 for high school graduates, 21 for junior college or vocational school graduates, and 23 for university

40

Table 2: Summary Statistics
Mean
Marital Status
0.8401
Completed Education
0.1690Large City
0.2587
Middle City
0.5712Rent
2.2323
Spouse availability
1.4686
Vacancy/Applicants
0.8464
Middle Income Class
0.5350High Income Class
0.1613
Mother’s Education
0.0400Father’s Education
0.1790Private High
0.3045Homemaker
0.3299Number of Siblings
2.4707
Having Brother(s)0.5804
Juku 20.3714
Juku 30.5889Juku 40.1821
Number of Professors
224.7074
Vacancy/Application
0.8751
University/High-School First Wage Ratio
1.2361EEOA
0.5789N of obs.
2,598

Std. Dev.
0.36660.37480.43800.49500.9384
0.46200.43380.49890.36790.19610.38340.46030.47030.9144
0.4936
0.48330.49210.386093.90150.43900.03650.4938

Min
0
0
0
0
0.97
0.44
0.12
0
0
0
0
0
0
1
0
0
0
0
12
0.09
1.15
0

Max
1
1
1
1
4.78
2.78
2.68
1
1
1
1
1
1
12
1
1
1
1
767
2.68
1.42
1

41

Table 3: Estimation Results: Bivariate Probit Model of Completed Education and Marital Status
(1)
Coef.
Education Equation
EEOA
Trend
Middle Income
High Income
Mother Education
Father Education
Private HighHomemaker
Sibling
Brother
Juku 2
Juku 3
Juku 4
Professor Ratio
Vacancy/Applicant
University/High-School First Wage Ratio
constant

0.15610.0792**0.21570.48850.87810.7274-0.1451
0.1493-0.1254-0.0973-0.0627-0.1992
0.7476-0.0003-0.15960.3796-1.8857

0.0791
0.09850.14720.08000.0721
0.06970.04530.07220.07020.07280.07870.00040.09981.24231.5602

***
***
***
***
**
**
***

Marriage Equation
Education
EEOA
Education × EEOA
Trend
Middle Income ClassHigh Income Class
Homemaker
Siblings
Brothers
Large City
Middle City
Rent
Spouse availability
Vacancy/Applicants
constant-0.32380.0802-0.3556

0.28080.10040.1898-0.0130-0.0495-0.0806
0.1662-0.1430-0.2177-0.1770-0.1782-0.1354-0.3272
1.40210.07730.11630.07280.04700.07270.11480.10050.08030.14620.10050.2849ρ

0.00780.1318N of obs.
Log Likelihood

(2)
Coef.

S.E.

***
***

***

***
**
*
*
**
***
***

2598
-1840.5758

S.E.

-0.00120.01540.2171
0.48740.87100.7225-0.1459
0.1571-0.1257-0.0961-0.0636-0.2109
0.7510-0.0002-0.1433
0.1828-1.74400.13010.01040.0791
0.09840.14750.08020.07210.07000.04530.07220.07020.07280.07860.00040.09991.24851.5649-0.2760
0.1678-0.3376-0.0117-0.0111-0.0507-0.0846
0.1668-0.1443-0.2206-0.1769-0.1662-0.0728-0.3348
1.37930.28600.14560.19170.01290.07730.11590.07290.04710.07280.11460.10040.08160.16740.10110.28910.00740.1306

***
***
***
***
**
**
***
***
***

*

***
**
*
*
**
***
***

2598
-1839.1309

Note: *** < 1%, ** < 5%, * < 10%. All equations are including district dummies.

42

Table 4: Partial Effects on Education and Marriage Decisions
Panel A
Direct EffectIndirect Effect

Estimate

Std. Err.

0.03090.04030.10290.23870.1831-0.02820.0305-0.0250-0.0195-0.0124-0.04020.1864-0.0001-0.03180.07570.01410.01160.02220.04880.02280.01540.01430.00970.01600.01570.01700.02300.00010.0191
0.2626

**
***
***
***
***
**
**
***

-0.14960.0844**0.0058-0.0030-0.0117-0.0191
0.0389-0.0331-0.0487-0.0389-0.0418-0.0317-0.0766

0.02400.01680.02910.01790.01020.01640.02890.02300.01880.03600.0250Total Effect

Estimate

Std. Err.

-0.0048-0.0063-0.0161-0.00480.00400.00310.00290.00370.00880.00290.00250.0030****-0.0368-0.02840.00450.00200.0064-0.0288
0.00000.0050-0.01200.01930.01530.00350.00260.00490.01560.00000.00370.0428**Estimate

Std. Err.

0.0010-0.0094-0.0278-0.02400.0429-0.0300

0.02360.01710.02740.01750.01010.0164

Education Decision
EEOA
Middle Income
High Income
Mother’s Education
Father’s Education
Private HighHomemaker
Number of Siblings
Having Brother(s)Juku 2
Juku 3
Juku 4
Number of Professors
Vacancy/Application
University/High-school First Wage Ratio
Marriage Decision
Completed Education
EEOA
Middle Income ClassHigh Income Class
Homemaker
Number of Siblings
Having Brother(s)Large City
Middle City
Rent
Spouse availability
Vacancy/Applicants
Mother’s Education
Father’s Education
Private High School
Juku 2
Juku 3
Juku 4
Number of Professors
Vacancy/Application
University/High-school First Wage Ratio

**
***
*

***
**
*
*
**

***
*

***

*

Note: *** < 1%, ** < 5%, * < 10%.

43

Table 4: Continued
Panel B
Direct EffectEducation Decision
EEOA
Trend
Middle Income
High Income
Mother’s Education
Father’s Education
Private HighHomemaker
Number of Sibling(s)Having Brother(s)Juku 2
Juku 3
Juku 4
Number of Professors
Vacancy/Application
University/High-school First Wage Ratio
Marriage Decision
Education
EEOA
TREND
Middle Income ClassHigh Income Class
Homemaker
Number of Siblings
Having Brother(s)Large City
Middle City
Rent
Spouse availability
Vacancy/Application
Mother’s Education
Father’s Education
Private High
Juku 2
Juku 3
Juku 4
Number of Professors
Vacancy/Application
University/High-school First Wage Ratio

Estimate

Std. Err.

-0.00020.00310.04050.1024
0.23600.1814-0.0283
0.0321-0.0250-0.0192-0.0126-0.04250.18710.0000-0.02850.03640.02510.00230.01160.0221
0.04840.02250.01550.01420.00970.01610.01560.01740.02300.00010.01950.2678-0.13210.0277-0.0028-0.0026-0.0120-0.02020.0392-0.0336-0.0496-0.0389-0.0391-0.0171-0.0787

0.08410.03740.00310.01680.02900.04030.01030.01640.02900.02300.01950.04030.0253

Indirect EffectTotal Effect

Estimate

Std. Err.

Estimate

Std. Err.

0.0000-0.0004-0.0057-0.0142-0.00450.00350.00270.00380.00040.00370.00910.00290.00250.00280.0277-0.0032-0.0083-0.0263-0.0247
0.0428-0.0309

0.03750.00310.01710.02740.01740.01010.0164-0.0322-0.02490.00400.00180.0060-0.0257
0.00000.0040-0.00510.01910.01540.00340.00240.00500.01570.00000.00360.0399

***
***
***
***
*
**
**
**
***

***
**
*
*
**
***

*

Note: *** < 1%, ** < 5%, * < 10%.

44*****